Uncertain seas: probabilistic modeling of future coastal flood zones

Abstract

Future sea-level rise will likely expand the inland extent of storm surge inundation and, in turn, increase the vulnerability of the people, properties and economies of coastal communities. Modeling future storm surge inundation enhanced by sea-level rise uses numerous data sources with inherent uncertainties. There is uncertainty in (1) hydrodynamic storm surge models, (2) future sea-level rise projections, and (3) topographic digital elevation models representing the height of the coastal land surface. This study implemented a Monte Carlo approach to incorporate the uncertainties of these data sources and model the future 1% flood zone extent in the Tottenville neighborhood of New York City (NYC) in a probabilistic, geographical information science (GIS) framework. Generated spatiotemporal statistical products indicate a range of possible future flood zone extents that results from the uncertainties of the data sources and from the terrain itself. Small changes in the modeled land and water heights within the estimated uncertainties of the data sources results in larger uncertainty in the future flood zone extent in low-lying areas with smaller terrain slope. An interactive web map, UncertainSeas.com, visualizes these statistical products and can inform coastal management policies to reduce the vulnerability of Tottenville, NYC to future coastal inundation.